Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison

Yang, Xiao-Hui1; Zhou, Yuanyuan1; Han, Peng2,3,4; Feng, Xuping2,3,4; Chen, Xiaofei2,3,4

2024-06-01

10.1007/s10712-024-09826-y

SURVEYS IN GEOPHYSICS   影响因子和分区

0169-3298

1573-0956

Rayleigh wave exploration is a powerful method for estimating near-surface shear-wave (S-wave) velocities, providing valuable insights into the stiffness properties of subsurface materials inside the Earth. The dispersion curve inversion of Rayleigh wave corresponds to the optimization process of searching for the optimal solutions of earth model parameters based on the measured dispersion curves. At present, diversified inversion algorithms have been introduced into the process of Rayleigh wave inversion. However, limited studies have been conducted to uncover the variations in inversion performance among commonly used inversion algorithms. To obtain a comprehensive understanding of the optimization performance of these inversion algorithms, we systematically investigate and quantitatively assess the inversion performance of two bionic algorithms, two probabilistic algorithms, a gradient-based algorithm, and two neural network algorithms. The evaluation indices include the computational cost, accuracy, stability, generalization ability, noise effects, and field data processing capability. It is found that the Bound-constrained limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS-B) algorithm and the broad learning (BL) network have the lowest computational cost among candidate algorithms. Furthermore, the transitional Markov Chain Monte Carlo algorithm, deep learning (DL) network, and BL network outperform the other four algorithms regarding accuracy, stability, resistance to noise effects, and capability to process field data. The DL and BL networks demonstrate the highest level of generalization compared to the other algorithms. The comparison results reveal the variations in candidate algorithms for the inversion task, causing a clear understanding of the inversion performance of candidate algorithms. This study can promote the S-wave velocity estimation by Rayleigh wave inversion.

Rayleigh wave inversion Near-surface S-wave velocity Optimization algorithm Inversion performance comparison

SCI ; EI

英语

通讯

Talent Launch Project of Chengdu University of Information Technology["KYTZ2023035","KYTZ202220"] ; Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology[2022B1212010002] ; Research Project on Disciplinary Development Strategy, Academic Divisions of the Chinese Academy of Sciences["XK2018DXA001","XK2018DXC003"] ; null[2023YFE0101800]

Geochemistry & Geophysics

Geochemistry & Geophysics

WOS:001228560700002

SPRINGER

GEOSCIENCES

Web of Science

1.Chengdu Univ Informat Technol, Sch Atmospher Sci, Plateau Atmosphere & Environm Key Lab Sichuan Prov, Chengdu 610225, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Deep Offshore Oil & Gas Explorat, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Guangdong Prov Key Lab Geophys High Resolut Imagin, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China

Yang, Xiao-Hui,Zhou, Yuanyuan,Han, Peng,et al. Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison[J]. SURVEYS IN GEOPHYSICS,2024,45(3).

Yang, Xiao-Hui,Zhou, Yuanyuan,Han, Peng,Feng, Xuping,&Chen, Xiaofei.(2024).Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison.SURVEYS IN GEOPHYSICS,45(3).

Yang, Xiao-Hui,et al."Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison".SURVEYS IN GEOPHYSICS 45.3(2024).